Biomedical engineering online · 2026
Convolutional neural network analysis of cervical CT images: classification of Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis.
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Kim Gun Ho, Sung Eui-Suk, Nam Kyoung Won
Аннотация
BACKGROUND: Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis are common diseases affecting the head and neck. The causes and treatment methods differ; however, the initial symptoms of these diseases (fever, pain, and neck swelling) are generally similar; therefore, it is important to accurately determine the type of disease at its initial stage.
RESULTS: During the performance evaluation, the values of precision-recall area under the curve (PR AUC) were 0.785, 0.731, 0.920, and 0.821 for the four single-disease detection models for Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis, respectively; 0.819 for a model for classifying the type of treatment; and 0.807 for a model for classifying all diseases with a single inspection. In the model performance tests, all six implemented models showed relatively high performance for the test dataset (accuracy: 0.6864-0.9278, precision: 0.7532-0.9583, recall: 0.3896-0.8804, and F1-score: 0.5310-0.8710).
CONCLUSION: Based on these experimental results, we conclude that the proposed CNN-based diagnosis support technique has the potential to be an efficient pre-screening tool for first-time patients with Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis by reducing the dependency on high-risk invasive diagnosis; however, additional model enhancement is required to improve its clinical applicability.
RESULTS: During the performance evaluation, the values of precision-recall area under the curve (PR AUC) were 0.785, 0.731, 0.920, and 0.821 for the four single-disease detection models for Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis, respectively; 0.819 for a model for classifying the type of treatment; and 0.807 for a model for classifying all diseases with a single inspection. In the model performance tests, all six implemented models showed relatively high performance for the test dataset (accuracy: 0.6864-0.9278, precision: 0.7532-0.9583, recall: 0.3896-0.8804, and F1-score: 0.5310-0.8710).
CONCLUSION: Based on these experimental results, we conclude that the proposed CNN-based diagnosis support technique has the potential to be an efficient pre-screening tool for first-time patients with Kikuchi disease, lymphoma, lymphadenitis, and tuberculosis by reducing the dependency on high-risk invasive diagnosis; however, additional model enhancement is required to improve its clinical applicability.